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arXiv:2605.27371cs.CY

Algorithmic Monocultures in Hiring

Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, Percy Liang

Abstract

Many employers screen job applicants with algorithms built by the same few algorithm vendors. We hypothesize that algorithmic monoculture leads to the same individuals and members of the same racial groups facing rejection. We acquire and analyze a novel dataset of 3 million applicants submitting 4 million applications where all the applications are screened by algorithms built by the same vendor. We find clear racial disparities in applicant outcomes. Of all applications submitted by Asian and Black applicants, 14.74% and 25.87% are submitted to positions that adversely impact Asian and Black applicants, respectively, according to U.S. employment discrimination standards. Individuals also receive homogeneous outcomes: 4% of all applicants who apply to 10 positions are recommended for rejection from all positions, a rate higher than expected by chance. To better understand this homogeneity, we leverage the deterministic replicability of hiring algorithms to generate the outcomes applicants would have received if they applied to all positions. We show that applicants would need to apply widely in order to ensure their applications are considered by a human

arXiv comments
Published at FAccT 2026. Website: https://algorithmichiring.github.io/
Journal reference
DOI
Subjects
cs.CYComputers and Society cs.AIArtificial Intelligence

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Linked by 6 identifiable commenters in 6 comments across 2 threads, from Jun 2026 to Jun 2026. Found in 1 HN submission from Jun 2026 to Jun 2026. Open submission discussions.

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Discussed in comments2 threads

  1. Algorithmic Monocultures in Hiring 5 comments

    • The paper is here: https://arxiv.org/pdf/2605.27371 They find "disparate impact" of pymetrics across racial groups, but it doesn't seem like they controlled for anything.

      verteuView on HNParent context

    • Misleading title the paper [0] does not mention any CV screening that might suggest racial or gender bias. It is purely about assessment tool. No AI or LLMs. I'm not saying AI is not biased, but this study does not prove that. [0] https://arxiv.org/pdf/2605.27371 From the paper…

      OrasView on HNParent context

    • Your summary of the EEOC guidance is correct. The problem is that the study here is using the four-fifths rule as a measurement of discrimination, instead of as a flag that triggers further investigation. It's in section 3.1 of the paper: https://arxiv.org/pdf/2605.27371.…

      rayinerView on HNParent context

    • I’ve rechecked it, and I still think I’m right. What am I missing? This is the paper under discussion: https://arxiv.org/pdf/2605.27371

      petesergeantView on HNParent context

    • Capitulation is a bad counterpropaganda tactic, especially with terms that have well defined domain specific meanings. Note that the OP uses "systemic rejection", while the paper does reference bias, it is in the precise meaning of the word.[0] And this is not targeted at the…

      nyrikkiView on HNParent context

  2. Algorithmic Monocultures in Hiring 1 comment

    • From a glance at the paper (https://arxiv.org/pdf/2605.27371) it looks like they're using self-reported ethnicity. "We only attempt to identify adverse impact based on self-reported racial information and do not impute data, noting that we do not have access to features like…

      madcaptenorView on HNParent context

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